This course covers Decision Trees and Ensemble Methods, aiming to teach students the fundamentals of decision trees, impurity functions, the CART algorithm, and basic properties of decision trees. Students will also learn about bias-variance trade-off, ExtraTrees algorithm, bagging, random forests, feature importance, and boosting techniques like gradient boosting, XGBoost, LightGBM, and CatBoost. The course utilizes a combination of lectures, examples, and hands-on practice with tools like Sklearn to help learners understand and apply these concepts effectively. The course is designed for individuals interested in machine learning, data science, or predictive modeling.
Overview
Syllabus
DTEL1 - 1 Welcome to DTE class.
DTEL1 - 2 Class Introduction.
DTEL1 - 3 Introduction to Decision Trees.
DTEL1 - 4 Impurity Functions.
DTEL1 - 5 CART Algorithm.
DTEL1 - 6 Basic Properties of Decision Trees.
DTEL1 7 Basic Regularization of Trees.
DTEL1 - 8 Sklearn Trees.
DTEL1 - 9 Conclusion.
DTEL2 2 1 Introduction.
DTEL2 2 2 Bias variance trade off.
DTEL2 2 3 Bias variance Decomposition.
DTEL2 2 4 Generalizations of bias variance tradeoff.
DTEL2 2 5 ExtraTrees Algorithm.
DTEL2 2 6 ExtraTrees with Sklearn.
DTEL2 2 7 Conclusion.
DTEL3 3 1 Introduction.
DTEL3 3 2 Bootstrap.
DTEL3 3 3 Bagging.
DTEL3 3 4 1 Example.
DTEL3 3 5 Random Forest.
DTEL3 3 4 2 Example Notebook.
DTEL3 3 6 General Ensembling.
DTEL4 4 1 Introduction.
DTEL4 4 2 Proximities.
DTEL4 4 3 Proximities Visualizations.
DTEL4 4 4 Feature Importance's.
DTEL4 4 5 Limitations of Tree Feature Importance.
DTEL4 4 6 Feature Importance's in Random Forest.
DTEL4 4 7 Summary.
DTEL5 5 1 Introduction.
DTEL5 5 2 Boosting.
DTEL5 5 3 Gradient Boosting.
DTEL5 5 4 XGBoost.
DTEL5 5 5 LightGBM.
DTEL5 5 6 CatBoost.
Taught by
Machine Learning University
